Real-time Sound Anomaly Detection in Industrial Environments with Deep Learning
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Date
2024
Authors
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Journal ISSN
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Publisher
Faculty of Engineering and Natural Science
Abstract
This research uses deep learning to explore the field of sound anomaly detection in industrial settings in response to the growing need for improved industrial efficiency and safety. Centered on taking care of the constraints of conventional techniques, the study examines the effectiveness of several deep learning architectures, such as hybrid models, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), in identifying abnormal noises. With a focus on rigorous evaluation of datasets, preprocessing methods, and benchmarks, the survey offers a thorough picture of the most recent models and their uses in a variety of industrial areas. This research compares deep learning with traditional methods for sound anomaly identification and looks at performance evaluation criteria. Case studies and realworld implementations demonstrate the usefulness of the enhancements. While highlighting the need for innovative approaches to enhance the practical usefulness and robustness of deep learning-based sound anomaly detection in industrial settings, the research also points out its shortcomings and makes recommendations for future directions. This research not only contributes valuable insights into the intersection of deep learning and industrial sound analysis but also serves as a pivotal guide for researchers and practitioners seeking to navigate the complexities of deploying effective sound anomaly detection systems.
Description
Keywords
Sound Anomaly Detection, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Hybrid Models
Citation
Zhailau M / Real-time Sound Anomaly Detection in Industrial Environments with Deep Learning / 2024 / 7M06102 - Computer Science